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AskMe: joint individual-level and community-level behavior interaction for question recommendation

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Abstract

Questions in Community Question Answering (CQA) sites are recommended to users, mainly based on users’ interest extracted from questions that users have answered or have asked. However, there is a general phenomenon that users answer fewer questions while pay more attention to follow questions and vote answers. This can impact the performance when recommending questions to users (for obtaining their answers) by using their historical answering behaviors on existing studies. To address the data sparsity issue, we propose AskMe, which aims to leverage the rich, hybrid behavior interactions in CQA to improve the question recommendation performance. On the one hand, we model the rich correlations between the user’s diverse behaviors (e.g., answer, follow, vote) to obtain the individual-level behavior interaction. On the other hand, we model the sophisticated behavioral associations between similar users to obtain the community-level behavior interaction. Finally, we propose the way of element-level fusion to mix these two kinds of interactions together to predict the ranking scores. A dataset collected from Zhihu (1126 users, 219434 questions) is utilized to evaluate the performance of the proposed model, and the experimental results show that our model has gained the best performance compared to baseline methods, especially when the historical answering behaviors data is scarce.

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Notes

  1. https://quoraconsulting.com/

  2. https://www.zhihu.com/

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Acknowledgements

This work was partially supported by the National Key R&D Program of China (2019QY0600), the National Natural Science Foundation of China (No.62025205, 62002292, 61725205, 61960206008), and Natural Science Basic Research Plan in Shaanxi Province of China (2020JQ-207).

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Authors and Affiliations

  1. Northwestern Polytechnical University, Xi’an, China

    Nuo Li, Bin Guo, Yan Liu, Jiaqi Liu & Zhiwen Yu

  2. The University of New South Wales, Sydney, Australia

    Lina Yao

  3. Peng Cheng Laboratory, Xi’an, China

    Nuo Li & Bin Guo

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  1. Nuo Li
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  2. Bin Guo
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  3. Yan Liu
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  4. Lina Yao
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  5. Jiaqi Liu
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  6. Zhiwen Yu
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Correspondence to Bin Guo.

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Li, N., Guo, B., Liu, Y. et al. AskMe: joint individual-level and community-level behavior interaction for question recommendation. World Wide Web 25, 49–72 (2022). https://doi.org/10.1007/s11280-021-00964-6

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